Industrial controllers are special-purpose computers utilized for controlling industrial processes, manufacturing equipment, and other factory automation, such as data collection or networked systems. At the core of the industrial control system, is a logic processor such as a Programmable Logic Controller (PLC) or PC-based controller. Programmable Logic Controllers for instance, are programmed by systems designers to operate manufacturing processes via user-designed logic programs or user programs. The user programs are stored in memory and generally executed by the PLC in a sequential manner although instruction jumping, looping and interrupt routines, for example, are also common. Associated with the user program are a plurality of memory elements or variables that provide dynamics to PLC operations and programs. Differences in PLCs are typically dependent on the number of Input/Output (I/O) they can process, amount of memory, number and type of instructions, and speed of the PLC central processing unit (CPU).
In a more macro sense than the controller, businesses have become more complex in that higher order business systems or computers often need to exchange data with such controllers. For instance, an industrial automation enterprise may include several plants in different locations. Modern drivers such as efficiency and productivity improvement, and cost-reduction, are requiring manufacturers to collect, analyze, and optimize data and metrics from global manufacturing sites. For example, a food company can have several plants located across the globe for producing a certain brand of food. These factories in the past were standalone, with minimum data collection and comparison of metrics with other similar factories. In the networked world of today, manufacturers are demanding real-time data from their factories to drive optimization and productivity. Unfortunately, conventional control systems architectures are not equipped to allow a seamless exchange of data between these various components of the enterprise.
Another requirement of modern control system architectures is the ability to record and store data in order to maintain compliance with Food and Drug Administration regulations such as Regulation 21 CFR Part 11. One common solution for recording data includes providing a local recording module that often occupies a slot in a controller backplane such as a PC-Historian which is an industrial computer for the controller backplane, and employs a transitional layer to supply an indirect interface to the controller. This includes a platform that provides high speed, time series, data storage and retrieval with both local and remote control processors. The PC-Historian communicates with controllers directly through the backplane and can communicate remotely via a network interface. The PC-Historian allows archiving data from the controller to an Archive Engine which provides additional storage capabilities.
In general, conventional historian processors enable high-speed real-time data collection by communicating directly with the control processor across the backplane for fast data collection speeds. This includes handling large quantities of data over extended time periods while providing efficient storage and retrieval of process data over extended periods of time. These solutions are generally employed for electronic documentation and provide an audit trail and data flags for tracking modified, inserted, or incomplete data. In order to configure such products, a Graphical User Interface (GUI) can be provided to map controller tags defined in a local or remote processor to a data historian file.
However, there are several disadvantages with existing data collection and storage solutions. Conventional PC-historians are generally applied on the back-end of system design and are thus loosely coupled or integrated within the framework of the control architecture. Such an arrangement leads to many inefficiencies for collecting data and ultimately identifying what data should or should not be captured. Other shortcomings include issues related to which historians should collect what data, and how such historians map and integrate into a larger enterprise.